Real world scenes are colorful and usually contain continuous color shades. To perfectly reproduce these scenes on display devices, the displays have to have a broad enough dynamic range and a high accuracy. The 24-bit RGB color space is commonly used in virtually every computer system as well as in television systems, video systems, etc. In order to be displayed on these 24-bit RGB displays, images resulting from a higher precision capturing or processing system have to be first quantized to 3×8 bit RGB true color signals. Representing color data with more than eight bits per channel using these 8-bit displays, and maintaining the video quality at the same time, is a focus of the present invention.
There have been efforts in using less bit images to represent more bit images in printing community. Halftoning algorithms are used to transform continuous-tone images to binary images to be printed by either a laser or inkjet printer. Two categories of halftoning algorithms are primarily used: dithering and error diffusion. Both methods capitalize on the low pass characteristic of the human visual system and redistribute quantization errors to high frequencies that are less noticeable to a human viewer. The major difference between dithering and error diffusion is that the dithering makes decisions pixel-by-pixel based on the pixel's coordinate, whereas the error diffusion algorithm makes decisions on the basis of a running error. Therefore, for the hardware implementation of the halftoning algorithms, more memory is required for error diffusion than for the dithering.
At the same time, there is another characteristic of human visual system which can be applied to obtain better perception of shades. This is based on the fact that human vision is much more sensitive in luminance than in chrominance. This characteristic makes it possible to manipulate the quantized color signals so that we can preserve higher precision of luminance while keeping the difference of the chrominance signals within a tolerable range.